Loading field map
For much of the late twentieth century, organizations treated digital technology as a support function—a tool for automating back-office processes, improving operational efficiency, or delivering information to managers. By the early 2000s, however, the rise of the internet, e-commerce, and enterprise systems had begun to erode that assumption. Executives and scholars alike started asking a more fundamental question: what happens when digital technology becomes not just an enabler of strategy but the very terrain on which strategy is made? This question defines the subfield of digital strategy. It has driven a sequence of frameworks that progressively shifted attention from measuring digital readiness to orchestrating ecosystems, leveraging data, and embedding algorithms into competitive logic.
The first structured responses to the new digital landscape were diagnostic. Digital Maturity Models emerged around 2000 as a way for organizations to assess where they stood in adopting digital capabilities. Borrowing from earlier capability maturity models in software engineering, these frameworks defined stages—from basic awareness to advanced integration—and offered benchmarks for progress. Their distinctive contribution was to make digital capability visible and measurable. Yet they carried a limitation: by focusing on staged progression, they implied that digital strategy was a linear journey of improvement rather than a fundamental rethinking of business models.
Digital Transformation arose in the same period but took a different stance. Rather than measuring maturity, it argued that organizations needed to undergo deep, holistic change—reconfiguring processes, culture, customer relationships, and value propositions around digital possibilities. Where maturity models offered a ladder, transformation offered a rupture. The two frameworks coexisted for years, with practitioners often using maturity models as a diagnostic prelude to transformation initiatives. But transformation’s emphasis on discontinuous change gradually overshadowed the incrementalism of maturity models, especially as industries like retail, media, and finance faced existential threats from born-digital competitors.
By the early 2010s, it became clear that transformation was not enough if it remained separate from core strategic thinking. Digital Business Strategy emerged around 2013 to collapse the long-standing divide between business strategy and IT strategy. Its central claim was that digital resources—data, platforms, algorithms, user networks—were no longer just inputs to strategy but the very substance of competitive advantage. A firm’s digital business strategy was not a plan for its IT department; it was the strategy itself, expressed through digital means. This framework extended the transformation agenda by insisting that digital change must be fused with strategic intent, not treated as a separate initiative.
At roughly the same time, Digital Innovation focused attention on the generative capacity of digital technology. Where transformation emphasized organizational change and business strategy emphasized competitive positioning, digital innovation asked how firms could create new products, services, and business models by recombining digital components. It drew on concepts like modularity, generativity, and digital options to explain why digital technologies enable rapid experimentation and novel value creation. Digital innovation complemented digital business strategy by providing a mechanism for renewal, but it also narrowed the focus: not every digital strategy needed to be innovative in the sense of creating new offerings; some firms succeeded by digitizing existing processes more effectively.
Platform Strategy entered the conversation around 2010 and introduced a fundamentally different logic. Instead of viewing the firm as a pipeline that creates value through linear production and delivery, platform strategy saw value creation as occurring through network effects among producers and consumers on a digital infrastructure. Platforms like Amazon, Alibaba, Uber, and Airbnb demonstrated that orchestrating an ecosystem could generate returns far beyond what a traditional firm could achieve alone. Platform strategy coexisted with digital business strategy, but it shifted the unit of analysis from the firm to the multi-sided market. This created a tension: digital business strategy assumed that competitive advantage resided in firm-specific resources and capabilities, while platform strategy argued that advantage came from controlling the architecture of interactions. The two frameworks remain in productive disagreement today, with some scholars attempting to reconcile them through concepts like platform-based ecosystems.
As digital platforms generated vast amounts of user data, a new question emerged: how should data itself become a strategic asset? Data-Driven Strategy crystallized around 2015, arguing that organizations could gain competitive advantage by systematically collecting, analyzing, and monetizing data. It extended digital business strategy by identifying data as a unique resource—non-rival, reusable, and capable of generating insights that improve over time. Data-driven strategy also narrowed the focus of digital transformation: rather than changing everything, a firm could concentrate on building data capabilities and analytics cultures. It coexisted with platform strategy, since platforms are natural data collectors, but it also applied to non-platform firms that could leverage internal or purchased data.
AI Strategy emerged around 2018 as a further specialization. While data-driven strategy treated data as the primary asset, AI strategy argued that the real value lay in the algorithms and models that learn from data to make predictions, automate decisions, and personalize experiences. AI strategy built on data-driven strategy but added a distinctive emphasis on machine learning infrastructure, model governance, and the competitive dynamics of algorithmic advantage. It also revived a tension from the transformation era: implementing AI at scale requires deep organizational change—new roles, ethical frameworks, and decision-making processes—that goes beyond data analytics. AI strategy thus reconnected with the holistic ambition of digital transformation while pushing into new technical and ethical territory.
Today, all seven frameworks remain active, but they have settled into a division of labor. Digital Maturity Models are still used for benchmarking and gap analysis, especially in less digitally advanced sectors. Digital Transformation continues as a broad umbrella for organizational change initiatives, though it increasingly incorporates insights from the later frameworks. Digital Business Strategy provides the foundational logic for treating digital as strategic, while Digital Innovation offers tools for product and business model experimentation. Platform Strategy dominates discussions of ecosystem competition and network effects. Data-Driven Strategy guides analytics and data governance investments, and AI Strategy is the fastest-growing area, shaping everything from customer engagement to supply chain optimization.
The leading frameworks agree on several points: digital technology is now central to competitive advantage; strategy must be dynamic and adaptive; and data and algorithms are increasingly critical resources. But they disagree sharply on the primary driver of value. Digital Transformation and Digital Innovation emphasize organizational change and creativity. Digital Business Strategy and Platform Strategy focus on resource configuration and ecosystem architecture. Data-Driven Strategy and AI Strategy prioritize data and algorithms as the ultimate sources of advantage. This disagreement is not a sign of weakness; it reflects the multidimensional nature of digital strategy itself. A firm that succeeds in one dimension may fail in another, and the frameworks help managers and scholars diagnose which dimension matters most in a given context.
From the early maturity models to today’s AI-infused strategies, the subfield has moved from asking “how digital are we?” to asking “how should digital reshape what we do, how we compete, and who we become?” That trajectory—from measurement to transformation to ecosystem orchestration to algorithmic strategy—captures the intellectual evolution of digital strategy as a field of inquiry.